Senior ML Systems Engineer for Scalable AI Platforms

Aimlroles; Sydney; Hybrid; AUD 180,000 - 240,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Xero is seeking a Machine Learning Engineer to lead the design and implementation of large-scale, production-grade distributed systems powering AI features for millions of daily users. You will own architecture decisions, reduce technical debt, and drive strategy across the AI Products estate.

You will mentor engineers, uplift data usability, and collaborate with Applied Scientists to productionize ML models and LLM-based features.

Qualifications

• 5+ years building and operating production Python services at scale.
• Own services or pipelines in production, including on-call responsibility and incident response.
• Deep understanding of distributed processing principles (Spark, Dask) and strong SQL capabilities.
• Experience integrating ML models or LLM-based features into production systems.
• Familiarity with ML tooling such as MLFlow, TensorFlow, or PyTorch and data orchestration tools like Airflow or Prefect.

Responsibilities

• Design and implement large-scale distributed production systems for AI features.
• Own architecture decisions, manage technical debt across AI Products estate.
• Mentor junior engineers and collaborate to lift the team's capabilities.
• Collaborate across the organization to enhance data usability and reliability.
• Deploy and operate in production environments on AWS and Kubernetes, integrating ML/LLM features.

Skills

Python at scale

Distributed systems

SQL proficiency

On-call / incident response

ML model integration

LLM tooling

Spark/Dask

Tools

Spark

Dask

Airflow

Prefect

MLFlow

TensorFlow

PyTorch

Job description

Xero is seeking a Machine Learning Engineer to lead the design and implementation of large-scale, production-grade distributed systems powering AI features for millions of daily users. You will own architecture decisions, reduce technical debt, and drive strategy across the AI Products estate.

You will mentor engineers, uplift data usability, and collaborate with Applied Scientists to productionize ML models and LLM-based features.